A Systematic Assessment of the Quality of Smartphone Applications for Gastroesophageal Reflux Disease
Bibliographic record
Abstract
Background and Aims: Smartphone applications aimed at patients with gastroesophageal reflux disease (GERD) have been downloaded more than 100,000 times, yet no systematic assessment of their quality has been completed. This study aimed to objectively assess the quality of GERD smartphone applications for patient education and disease management. Methods: The Apple App Store and Google Play Store were systematically searched for relevant applications. Two independent reviewers performed the application screening and eligibility assessment. Included applications were graded using the validated Mobile Application Rating Scale, which encompasses 4 domains (engagement, functionality, aesthetics, and information) as well as an overall application quality score. The associations between overall application quality, user ratings and download numbers were evaluated. Results: < .001). There was no correlation between graded quality and either user ratings or the number of downloads. Conclusion: While numerous smartphone applications exist to support patients with GERD, their quality is variable. Patient education applications are of particularly low quality. Our findings can help to inform the selection of applications by patients and guide clinicians' recommendations. This study also highlights the need for higher-quality, evidence-informed applications aimed at GERD patient education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".